
"""simple docstring"""


from __future__ import annotations

import unittest

from transformers import DebertaVaConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
      import tensorflow as tf

      from transformers import (
          TFDebertaVaForMaskedLM,
          TFDebertaVaForQuestionAnswering,
          TFDebertaVaForSequenceClassification,
          TFDebertaVaForTokenClassification,
          TFDebertaVaModel,
      )



class 	__a	:






       def __init__(	self			:				Tuple							,   UpperCAmelCase_			:				str							,   UpperCAmelCase_			:				Dict=13							,   UpperCAmelCase_			:				Any=7							,   UpperCAmelCase_			:				Tuple=True							,   UpperCAmelCase_			:				Optional[int]=True							,   UpperCAmelCase_			:				Dict=True							,   UpperCAmelCase_			:				Any=True							,   UpperCAmelCase_			:				Union[str, Any]=99							,   UpperCAmelCase_			:				str=32							,   UpperCAmelCase_			:				Union[str, Any]=2							,   UpperCAmelCase_			:				List[Any]=4							,   UpperCAmelCase_			:				Dict=37							,   UpperCAmelCase_			:				Optional[int]="gelu"							,   UpperCAmelCase_			:				List[str]=0.1							,   UpperCAmelCase_			:				Tuple=0.1							,   UpperCAmelCase_			:				str=512							,   UpperCAmelCase_			:				Optional[int]=16							,   UpperCAmelCase_			:				int=2							,   UpperCAmelCase_			:				Dict=0.02							,   UpperCAmelCase_			:				int=False							,   UpperCAmelCase_			:				Tuple=True							,   UpperCAmelCase_			:				str="None"							,   UpperCAmelCase_			:				Dict=3							,   UpperCAmelCase_			:				Dict=4							,   UpperCAmelCase_			:				str=None							,   )->	Dict:







              """simple docstring"""
              UpperCamelCase       			=		parent
              UpperCamelCase       			=		batch_size
              UpperCamelCase       			=		seq_length
              UpperCamelCase       			=		is_training
              UpperCamelCase       			=		use_input_mask
              UpperCamelCase       			=		use_token_type_ids
              UpperCamelCase       			=		use_labels
              UpperCamelCase       			=		vocab_size
              UpperCamelCase       			=		hidden_size
              UpperCamelCase       			=		num_hidden_layers
              UpperCamelCase       			=		num_attention_heads
              UpperCamelCase       			=		intermediate_size
              UpperCamelCase       			=		hidden_act
              UpperCamelCase       			=		hidden_dropout_prob
              UpperCamelCase       			=		attention_probs_dropout_prob
              UpperCamelCase       			=		max_position_embeddings
              UpperCamelCase       			=		type_vocab_size
              UpperCamelCase       			=		type_sequence_label_size
              UpperCamelCase       			=		initializer_range
              UpperCamelCase       			=		num_labels
              UpperCamelCase       			=		num_choices
              UpperCamelCase       			=		relative_attention
              UpperCamelCase       			=		position_biased_input
              UpperCamelCase       			=		pos_att_type
              UpperCamelCase       			=		scope






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Optional[Any]      )->	List[str]:







              """simple docstring"""
              UpperCamelCase       			=		ids_tensor([self.batch_size, self.seq_length]							,   self.vocab_size      )

              UpperCamelCase       			=		None
              if self.use_input_mask:
                     UpperCamelCase       			=		random_attention_mask([self.batch_size, self.seq_length]      )

              UpperCamelCase       			=		None
              if self.use_token_type_ids:
                     UpperCamelCase       			=		ids_tensor([self.batch_size, self.seq_length]							,   self.type_vocab_size      )

              UpperCamelCase       			=		None
              UpperCamelCase       			=		None
              UpperCamelCase       			=		None
              if self.use_labels:
                     UpperCamelCase       			=		ids_tensor([self.batch_size]							,   self.type_sequence_label_size      )
                     UpperCamelCase       			=		ids_tensor([self.batch_size, self.seq_length]							,   self.num_labels      )

              UpperCamelCase       			=		DebertaVaConfig(
                  vocab_size=self.vocab_size							,   hidden_size=self.hidden_size							,   num_hidden_layers=self.num_hidden_layers							,   num_attention_heads=self.num_attention_heads							,   intermediate_size=self.intermediate_size							,   hidden_act=self.hidden_act							,   hidden_dropout_prob=self.hidden_dropout_prob							,   attention_probs_dropout_prob=self.attention_probs_dropout_prob							,   max_position_embeddings=self.max_position_embeddings							,   type_vocab_size=self.type_vocab_size							,   relative_attention=self.relative_attention							,   position_biased_input=self.position_biased_input							,   initializer_range=self.initializer_range							,   return_dict=UpperCAmelCase_							,   )

              return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Dict							,   UpperCAmelCase_			:				str							,   UpperCAmelCase_			:				Dict							,   UpperCAmelCase_			:				Optional[int]							,   UpperCAmelCase_			:				int							,   UpperCAmelCase_			:				Union[str, Any]							,   UpperCAmelCase_			:				int							,   UpperCAmelCase_			:				Optional[Any]      )->	Tuple:







              """simple docstring"""
              UpperCamelCase       			=		TFDebertaVaModel(config=UpperCAmelCase_      )
              UpperCamelCase       			=		{"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}

              UpperCamelCase       			=		[input_ids, input_mask]
              UpperCamelCase       			=		model(UpperCAmelCase_      )

              UpperCamelCase       			=		model(UpperCAmelCase_      )

              self.parent.assertEqual(result.last_hidden_state.shape							,   (self.batch_size, self.seq_length, self.hidden_size)      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Tuple							,   UpperCAmelCase_			:				Union[str, Any]							,   UpperCAmelCase_			:				Union[str, Any]							,   UpperCAmelCase_			:				int							,   UpperCAmelCase_			:				int							,   UpperCAmelCase_			:				Union[str, Any]							,   UpperCAmelCase_			:				Optional[int]							,   UpperCAmelCase_			:				Optional[int]      )->	int:







              """simple docstring"""
              UpperCamelCase       			=		TFDebertaVaForMaskedLM(config=UpperCAmelCase_      )
              UpperCamelCase       			=		{
                  "input_ids": input_ids,
                  "attention_mask": input_mask,
                  "token_type_ids": token_type_ids,
              }
              UpperCamelCase       			=		model(UpperCAmelCase_      )
              self.parent.assertEqual(result.logits.shape							,   (self.batch_size, self.seq_length, self.vocab_size)      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Dict							,   UpperCAmelCase_			:				Tuple							,   UpperCAmelCase_			:				Optional[int]							,   UpperCAmelCase_			:				Dict							,   UpperCAmelCase_			:				Tuple							,   UpperCAmelCase_			:				Optional[Any]							,   UpperCAmelCase_			:				str							,   UpperCAmelCase_			:				Any      )->	Tuple:







              """simple docstring"""
              UpperCamelCase       			=		self.num_labels
              UpperCamelCase       			=		TFDebertaVaForSequenceClassification(config=UpperCAmelCase_      )
              UpperCamelCase       			=		{
                  "input_ids": input_ids,
                  "attention_mask": input_mask,
                  "token_type_ids": token_type_ids,
              }

              UpperCamelCase       			=		model(UpperCAmelCase_      )
              self.parent.assertEqual(result.logits.shape							,   (self.batch_size, self.num_labels)      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Dict							,   UpperCAmelCase_			:				Dict							,   UpperCAmelCase_			:				str							,   UpperCAmelCase_			:				Optional[Any]							,   UpperCAmelCase_			:				List[Any]							,   UpperCAmelCase_			:				Union[str, Any]							,   UpperCAmelCase_			:				List[str]							,   UpperCAmelCase_			:				List[Any]      )->	Optional[int]:







              """simple docstring"""
              UpperCamelCase       			=		self.num_labels
              UpperCamelCase       			=		TFDebertaVaForTokenClassification(config=UpperCAmelCase_      )
              UpperCamelCase       			=		{
                  "input_ids": input_ids,
                  "attention_mask": input_mask,
                  "token_type_ids": token_type_ids,
              }
              UpperCamelCase       			=		model(UpperCAmelCase_      )
              self.parent.assertEqual(result.logits.shape							,   (self.batch_size, self.seq_length, self.num_labels)      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Union[str, Any]							,   UpperCAmelCase_			:				Tuple							,   UpperCAmelCase_			:				Union[str, Any]							,   UpperCAmelCase_			:				Dict							,   UpperCAmelCase_			:				Dict							,   UpperCAmelCase_			:				str							,   UpperCAmelCase_			:				Tuple							,   UpperCAmelCase_			:				Optional[int]      )->	Tuple:







              """simple docstring"""
              UpperCamelCase       			=		TFDebertaVaForQuestionAnswering(config=UpperCAmelCase_      )
              UpperCamelCase       			=		{
                  "input_ids": input_ids,
                  "attention_mask": input_mask,
                  "token_type_ids": token_type_ids,
              }

              UpperCamelCase       			=		model(UpperCAmelCase_      )
              self.parent.assertEqual(result.start_logits.shape							,   (self.batch_size, self.seq_length)      )
              self.parent.assertEqual(result.end_logits.shape							,   (self.batch_size, self.seq_length)      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				str      )->	Union[str, Any]:







              """simple docstring"""
              UpperCamelCase       			=		self.prepare_config_and_inputs()
              (
                  (
                  UpperCamelCase
              )			,							(
                  UpperCamelCase
              )			,							(
                  UpperCamelCase
              )			,							(
                  UpperCamelCase
              )			,							(
                  UpperCamelCase
              )			,							(
                  UpperCamelCase
              )			,							(
                  UpperCamelCase
              )			,							
              )       			=		config_and_inputs
              UpperCamelCase       			=		{"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
              return config, inputs_dict





@require_tf
class 	__a	(   _lowerCAmelCase     ,  _lowerCAmelCase     ,  unittest.TestCase						):
       UpperCamelCase_		:       Optional[int]				=      (
           (
               TFDebertaVaModel,
               TFDebertaVaForMaskedLM,
               TFDebertaVaForQuestionAnswering,
               TFDebertaVaForSequenceClassification,
               TFDebertaVaForTokenClassification,
           )
           if is_tf_available()
           else ()
       )
       UpperCamelCase_		:       str				=      (
           {
               '''feature-extraction''': TFDebertaVaModel,
               '''fill-mask''': TFDebertaVaForMaskedLM,
               '''question-answering''': TFDebertaVaForQuestionAnswering,
               '''text-classification''': TFDebertaVaForSequenceClassification,
               '''token-classification''': TFDebertaVaForTokenClassification,
               '''zero-shot''': TFDebertaVaForSequenceClassification,
           }
           if is_tf_available()
           else {}
       )

       UpperCamelCase_		:       Dict				=      False
       UpperCamelCase_		:       List[str]				=      False






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Union[str, Any]      )->	Any:







              """simple docstring"""
              UpperCamelCase       			=		TFDebertaVaModelTester(self      )
              UpperCamelCase       			=		ConfigTester(self							,   config_class=UpperCAmelCase_							,   hidden_size=37      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Optional[int]      )->	Union[str, Any]:







              """simple docstring"""
              self.config_tester.run_common_tests()






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Any      )->	Optional[Any]:







              """simple docstring"""
              UpperCamelCase       			=		self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_model(*UpperCAmelCase_      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Tuple      )->	Optional[int]:







              """simple docstring"""
              UpperCamelCase       			=		self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_for_masked_lm(*UpperCAmelCase_      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				List[Any]      )->	Any:







              """simple docstring"""
              UpperCamelCase       			=		self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_for_question_answering(*UpperCAmelCase_      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				List[Any]      )->	Any:







              """simple docstring"""
              UpperCamelCase       			=		self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_for_sequence_classification(*UpperCAmelCase_      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				int      )->	Any:







              """simple docstring"""
              UpperCamelCase       			=		self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_for_token_classification(*UpperCAmelCase_      )






       @slow
       def 						_SCREAMING_SNAKE_CASE							(	self			:				List[str]      )->	List[str]:







              """simple docstring"""
              UpperCamelCase       			=		TFDebertaVaModel.from_pretrained("kamalkraj/deberta-v2-xlarge"      )
              self.assertIsNotNone(UpperCAmelCase_      )





@require_tf
class 	__a	(   unittest.TestCase						):






       @unittest.skip(reason="Model not available yet"      )
       def 						_SCREAMING_SNAKE_CASE							(	self			:				Any      )->	List[Any]:







              """simple docstring"""
              pass






       @slow
       def 						_SCREAMING_SNAKE_CASE							(	self			:				Dict      )->	Tuple:







              """simple docstring"""
              UpperCamelCase       			=		TFDebertaVaModel.from_pretrained("kamalkraj/deberta-v2-xlarge"      )
              UpperCamelCase       			=		tf.constant([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]]      )
              UpperCamelCase       			=		tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]      )
              UpperCamelCase       			=		model(UpperCAmelCase_							,   attention_mask=UpperCAmelCase_      )[0]

              UpperCamelCase       			=		tf.constant(
                  [[[0.2356, 0.1948, 0.0369], [-0.1063, 0.3586, -0.5152], [-0.6399, -0.0259, -0.2525]]]      )
              tf.debugging.assert_near(output[:, 1:4, 1:4]							,   UpperCAmelCase_							,   atol=1e-4      )







"""simple docstring"""


from collections import defaultdict
from typing import Optional

from ..image_utils import load_image
from ..utils import (
    add_end_docstrings,
    is_torch_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ChunkPipeline


if is_torch_available():
      import torch

      from ..models.auto.modeling_auto import MODEL_FOR_MASK_GENERATION_MAPPING

SCREAMING_SNAKE_CASE			  =  logging.get_logger(__name__)



@add_end_docstrings(_lowerCAmelCase						)
class 	__a	(   _lowerCAmelCase						):






       def __init__(	self			:				str							,   **UpperCAmelCase_			:				List[str]      )->	Tuple:







              """simple docstring"""
              super().__init__(**UpperCAmelCase_      )
              requires_backends(self							,   "vision"      )
              requires_backends(self							,   "torch"      )

              if self.framework != "pt":
                     raise ValueError(f"The {self.__class__} is only available in PyTorch."      )

              self.check_model_type(UpperCAmelCase_      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Tuple							,   **UpperCAmelCase_			:				int      )->	List[str]:







              """simple docstring"""
              UpperCamelCase       			=		{}
              UpperCamelCase       			=		{}
              UpperCamelCase       			=		{}
              # preprocess args
              if "points_per_batch" in kwargs:
                     UpperCamelCase       			=		kwargs["points_per_batch"]
              if "points_per_crop" in kwargs:
                     UpperCamelCase       			=		kwargs["points_per_crop"]
              if "crops_n_layers" in kwargs:
                     UpperCamelCase       			=		kwargs["crops_n_layers"]
              if "crop_overlap_ratio" in kwargs:
                     UpperCamelCase       			=		kwargs["crop_overlap_ratio"]
              if "crop_n_points_downscale_factor" in kwargs:
                     UpperCamelCase       			=		kwargs["crop_n_points_downscale_factor"]
              # postprocess args
              if "pred_iou_thresh" in kwargs:
                     UpperCamelCase       			=		kwargs["pred_iou_thresh"]
              if "stability_score_offset" in kwargs:
                     UpperCamelCase       			=		kwargs["stability_score_offset"]
              if "mask_threshold" in kwargs:
                     UpperCamelCase       			=		kwargs["mask_threshold"]
              if "stability_score_thresh" in kwargs:
                     UpperCamelCase       			=		kwargs["stability_score_thresh"]
              if "crops_nms_thresh" in kwargs:
                     UpperCamelCase       			=		kwargs["crops_nms_thresh"]
              if "output_rle_mask" in kwargs:
                     UpperCamelCase       			=		kwargs["output_rle_mask"]
              if "output_bboxes_mask" in kwargs:
                     UpperCamelCase       			=		kwargs["output_bboxes_mask"]
              return preprocess_kwargs, forward_params, postprocess_kwargs






       def __call__(	self			:				Optional[int]							,   UpperCAmelCase_			:				Optional[int]							,   *UpperCAmelCase_			:				Tuple							,   UpperCAmelCase_			:				int=None							,   UpperCAmelCase_			:				Tuple=None							,   **UpperCAmelCase_			:				str      )->	Any:







              """simple docstring"""
              return super().__call__(UpperCAmelCase_							,   *UpperCAmelCase_							,   num_workers=UpperCAmelCase_							,   batch_size=UpperCAmelCase_							,   **UpperCAmelCase_      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Optional[int]							,   UpperCAmelCase_			:				Optional[int]							,   UpperCAmelCase_			:				str=64							,   UpperCAmelCase_			:				int = 0							,   UpperCAmelCase_			:				float = 512 / 1_500							,   UpperCAmelCase_			:				Optional[int] = 32							,   UpperCAmelCase_			:				Optional[int] = 1							,   )->	List[Any]:







              """simple docstring"""
              UpperCamelCase       			=		load_image(UpperCAmelCase_      )
              UpperCamelCase       			=		self.image_processor.size["longest_edge"]
              UpperCamelCase			,							UpperCamelCase			,							UpperCamelCase			,							UpperCamelCase       			=		self.image_processor.generate_crop_boxes(
                  UpperCAmelCase_							,   UpperCAmelCase_							,   UpperCAmelCase_							,   UpperCAmelCase_							,   UpperCAmelCase_							,   UpperCAmelCase_      )
              UpperCamelCase       			=		self.image_processor(images=UpperCAmelCase_							,   return_tensors="pt"      )

              with self.device_placement():
                     if self.framework == "pt":
                            UpperCamelCase       			=		self.get_inference_context()
                            with inference_context():
                                   UpperCamelCase       			=		self._ensure_tensor_on_device(UpperCAmelCase_							,   device=self.device      )
                                   UpperCamelCase       			=		self.model.get_image_embeddings(model_inputs.pop("pixel_values"      )      )
                                   UpperCamelCase       			=		image_embeddings

              UpperCamelCase       			=		grid_points.shape[1]
              UpperCamelCase       			=		points_per_batch if points_per_batch is not None else n_points

              if points_per_batch <= 0:
                     raise ValueError(
                         "Cannot have points_per_batch<=0. Must be >=1 to returned batched outputs. "
                         "To return all points at once, set points_per_batch to None"      )

              for i in range(0							,   UpperCAmelCase_							,   UpperCAmelCase_      ):
                     UpperCamelCase       			=		grid_points[:, i : i + points_per_batch, :, :]
                     UpperCamelCase       			=		input_labels[:, i : i + points_per_batch]
                     UpperCamelCase       			=		i == n_points - points_per_batch
                     yield {
                         "input_points": batched_points,
                         "input_labels": labels,
                         "input_boxes": crop_boxes,
                         "is_last": is_last,
                         **model_inputs,
                     }






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Union[str, Any]							,   UpperCAmelCase_			:				Union[str, Any]							,   UpperCAmelCase_			:				int=0.88							,   UpperCAmelCase_			:				Optional[int]=0.95							,   UpperCAmelCase_			:				Dict=0							,   UpperCAmelCase_			:				int=1							,   )->	Any:







              """simple docstring"""
              UpperCamelCase       			=		model_inputs.pop("input_boxes"      )
              UpperCamelCase       			=		model_inputs.pop("is_last"      )
              UpperCamelCase       			=		model_inputs.pop("original_sizes"      ).tolist()
              UpperCamelCase       			=		model_inputs.pop("reshaped_input_sizes"      ).tolist()

              UpperCamelCase       			=		self.model(**UpperCAmelCase_      )

              # post processing happens here in order to avoid CPU GPU copies of ALL the masks
              UpperCamelCase       			=		model_outputs["pred_masks"]
              UpperCamelCase       			=		self.image_processor.post_process_masks(
                  UpperCAmelCase_							,   UpperCAmelCase_							,   UpperCAmelCase_							,   UpperCAmelCase_							,   binarize=UpperCAmelCase_      )
              UpperCamelCase       			=		model_outputs["iou_scores"]
              UpperCamelCase			,							UpperCamelCase			,							UpperCamelCase       			=		self.image_processor.filter_masks(
                  masks[0]							,   iou_scores[0]							,   original_sizes[0]							,   input_boxes[0]							,   UpperCAmelCase_							,   UpperCAmelCase_							,   UpperCAmelCase_							,   UpperCAmelCase_							,   )
              return {
                  "masks": masks,
                  "is_last": is_last,
                  "boxes": boxes,
                  "iou_scores": iou_scores,
              }






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Any							,   UpperCAmelCase_			:				Tuple							,   UpperCAmelCase_			:				List[str]=False							,   UpperCAmelCase_			:				str=False							,   UpperCAmelCase_			:				Optional[int]=0.7							,   )->	Dict:







              """simple docstring"""
              UpperCamelCase       			=		[]
              UpperCamelCase       			=		[]
              UpperCamelCase       			=		[]
              for model_output in model_outputs:
                     all_scores.append(model_output.pop("iou_scores"      )      )
                     all_masks.extend(model_output.pop("masks"      )      )
                     all_boxes.append(model_output.pop("boxes"      )      )

              UpperCamelCase       			=		torch.cat(UpperCAmelCase_      )
              UpperCamelCase       			=		torch.cat(UpperCAmelCase_      )
              UpperCamelCase			,							UpperCamelCase			,							UpperCamelCase			,							UpperCamelCase       			=		self.image_processor.post_process_for_mask_generation(
                  UpperCAmelCase_							,   UpperCAmelCase_							,   UpperCAmelCase_							,   UpperCAmelCase_      )

              UpperCamelCase       			=		defaultdict(UpperCAmelCase_      )
              for output in model_outputs:
                     for k, v in output.items():
                            extra[k].append(UpperCAmelCase_      )

              UpperCamelCase       			=		{}
              if output_rle_mask:
                     UpperCamelCase       			=		rle_mask

              if output_bboxes_mask:
                     UpperCamelCase       			=		bounding_boxes

              return {"masks": output_masks, "scores": iou_scores, **optional, **extra}





